Colouring edges by weight in networkx

Question

I have only found something similar to what I want here:

Coloring networkx edges based on weight

However I can't seem to apply this to my problem. I have a graph with weighted edges, but the weights aren't unique (so there are like 15 edges with weight 1). I want to colour my edges based on the weight they have, the lower the weight the lighter the colour.

I tried to apply the method suggested in the above question, but from what I understand this requires the weights to be unique on each edge?

So far I've produced a list in ascending order of the different edge weights and wanted to use this to classify the possible edge colours. I'm trying to avoid drawing the edges by weight as I may need to draw a very large graph in the future with a huge range of weights on the edges.

If it's unclear let me know in comments and I'll give more specific info.

Thanks!

EDIT: def draw_graph(target): nlist = [target]+G.neighbors(target) H=nx.subgraph(G, nlist) n=H.number_of_edges() colours = range(n) labels,weights = colour_and_label_edges(H)

pos = nx.spring_layout(H)
nx.draw(H, pos, node_color='#A0CBE2',edge_color=colours, node_size=100, edge_cmap=plt.cm.Blues, width=0.5, with_labels=False)
nx.draw_networkx_edge_labels(H, pos, edge_labels=labels)
plt.savefig("Graphs/edge_colormap_%s.png" % target) # save as png
plt.show() # display
pass

def colour_and_label_edges(graph):
    d={}
    for (u,v) in graph.edges():
        d[u,v]=graph[u][v]['weight']
    temp=[]
    for val in d.values():
        if val not in temp:
            temp.append(val)
    weights = sorted(temp,key=int)
    return d, weights

The above code is incomplete, but the idea is the function gives me a list of the weights, as so:

[1, 2, 3, 4, 5, 6, 9, 10, 16, 21, 47, 89, 124, 134, 224]

I then want to use this list to assign each weight a colour, the higher the weight the darker the colour. (I've used a very small subgraph for this example relative to the data set). Hope that clears it up a little :S

Answer 1

You can use the edge weights and a colormap to draw them. You might want t a different colormap from the one below.

import matplotlib.pyplot as plt
import networkx as nx
import random

G = nx.gnp_random_graph(10,0.3)
for u,v,d in G.edges(data=True):
    d['weight'] = random.random()

edges,weights = zip(*nx.get_edge_attributes(G,'weight').items())

pos = nx.spring_layout(G)
nx.draw(G, pos, node_color='b', edgelist=edges, edge_color=weights, width=10.0, edge_cmap=plt.cm.Blues)
plt.savefig('edges.png')